# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved. import argparse import os import os.path as osp import torchvision.transforms.functional as TF import torch.nn.functional as F import cv2 import tempfile import imageio import torch import decord from PIL import Image import numpy as np from rembg import remove, new_session import random import ffmpeg import os import tempfile import subprocess import json from functools import lru_cache from PIL import Image video_info_cache = [] def seed_everything(seed: int): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) if torch.cuda.is_available(): torch.cuda.manual_seed(seed) if torch.backends.mps.is_available(): torch.mps.manual_seed(seed) def resample(video_fps, video_frames_count, max_target_frames_count, target_fps, start_target_frame ): import math video_frame_duration = 1 /video_fps target_frame_duration = 1 / target_fps target_time = start_target_frame * target_frame_duration frame_no = math.ceil(target_time / video_frame_duration) cur_time = frame_no * video_frame_duration frame_ids =[] while True: if max_target_frames_count != 0 and len(frame_ids) >= max_target_frames_count : break diff = round( (target_time -cur_time) / video_frame_duration , 5) add_frames_count = math.ceil( diff) frame_no += add_frames_count if frame_no >= video_frames_count: break frame_ids.append(frame_no) cur_time += add_frames_count * video_frame_duration target_time += target_frame_duration frame_ids = frame_ids[:max_target_frames_count] return frame_ids import os from datetime import datetime def get_file_creation_date(file_path): # On Windows if os.name == 'nt': return datetime.fromtimestamp(os.path.getctime(file_path)) # On Unix/Linux/Mac (gets last status change, not creation) else: stat = os.stat(file_path) return datetime.fromtimestamp(stat.st_birthtime if hasattr(stat, 'st_birthtime') else stat.st_mtime) def truncate_for_filesystem(s, max_bytes=255): if len(s.encode('utf-8')) <= max_bytes: return s l, r = 0, len(s) while l < r: m = (l + r + 1) // 2 if len(s[:m].encode('utf-8')) <= max_bytes: l = m else: r = m - 1 return s[:l] @lru_cache(maxsize=100) def get_video_info(video_path): global video_info_cache import cv2 cap = cv2.VideoCapture(video_path) # Get FPS fps = round(cap.get(cv2.CAP_PROP_FPS)) # Get resolution width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) cap.release() return fps, width, height, frame_count def get_video_frame(file_name: str, frame_no: int, return_last_if_missing: bool = False, return_PIL = True) -> torch.Tensor: """Extract nth frame from video as PyTorch tensor normalized to [-1, 1].""" cap = cv2.VideoCapture(file_name) if not cap.isOpened(): raise ValueError(f"Cannot open video: {file_name}") total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) # Handle out of bounds if frame_no >= total_frames or frame_no < 0: if return_last_if_missing: frame_no = total_frames - 1 else: cap.release() raise IndexError(f"Frame {frame_no} out of bounds (0-{total_frames-1})") # Get frame cap.set(cv2.CAP_PROP_POS_FRAMES, frame_no) ret, frame = cap.read() cap.release() if not ret: raise ValueError(f"Failed to read frame {frame_no}") # Convert BGR->RGB, reshape to (C,H,W), normalize to [-1,1] frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) if return_PIL: return Image.fromarray(frame) else: return (torch.from_numpy(frame).permute(2, 0, 1).float() / 127.5) - 1.0 # def get_video_frame(file_name, frame_no): # decord.bridge.set_bridge('torch') # reader = decord.VideoReader(file_name) # frame = reader.get_batch([frame_no]).squeeze(0) # img = Image.fromarray(frame.numpy().astype(np.uint8)) # return img def convert_image_to_video(image): if image is None: return None # Convert PIL/numpy image to OpenCV format if needed if isinstance(image, np.ndarray): # Gradio images are typically RGB, OpenCV expects BGR img_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) else: # Handle PIL Image img_array = np.array(image) img_bgr = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR) height, width = img_bgr.shape[:2] # Create temporary video file (auto-cleaned by Gradio) with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as temp_video: fourcc = cv2.VideoWriter_fourcc(*'mp4v') out = cv2.VideoWriter(temp_video.name, fourcc, 30.0, (width, height)) out.write(img_bgr) out.release() return temp_video.name def resize_lanczos(img, h, w): img = (img + 1).float().mul_(127.5) img = Image.fromarray(np.clip(img.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) img = img.resize((w,h), resample=Image.Resampling.LANCZOS) img = torch.from_numpy(np.array(img).astype(np.float32)).movedim(-1, 0) img = img.div(127.5).sub_(1) return img def remove_background(img, session=None): if session ==None: session = new_session() img = Image.fromarray(np.clip(255. * img.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) img = remove(img, session=session, alpha_matting = True, bgcolor=[255, 255, 255, 0]).convert('RGB') return torch.from_numpy(np.array(img).astype(np.float32) / 255.0).movedim(-1, 0) def convert_image_to_tensor(image): return torch.from_numpy(np.array(image).astype(np.float32)).div_(127.5).sub_(1.).movedim(-1, 0) def convert_tensor_to_image(t, frame_no = -1): t = t[:, frame_no] if frame_no >= 0 else t return Image.fromarray(t.clone().add_(1.).mul_(127.5).permute(1,2,0).to(torch.uint8).cpu().numpy()) def save_image(tensor_image, name, frame_no = -1): convert_tensor_to_image(tensor_image, frame_no).save(name) def get_outpainting_full_area_dimensions(frame_height,frame_width, outpainting_dims): outpainting_top, outpainting_bottom, outpainting_left, outpainting_right= outpainting_dims frame_height = int(frame_height * (100 + outpainting_top + outpainting_bottom) / 100) frame_width = int(frame_width * (100 + outpainting_left + outpainting_right) / 100) return frame_height, frame_width def get_outpainting_frame_location(final_height, final_width, outpainting_dims, block_size = 8): outpainting_top, outpainting_bottom, outpainting_left, outpainting_right= outpainting_dims raw_height = int(final_height / ((100 + outpainting_top + outpainting_bottom) / 100)) height = int(raw_height / block_size) * block_size extra_height = raw_height - height raw_width = int(final_width / ((100 + outpainting_left + outpainting_right) / 100)) width = int(raw_width / block_size) * block_size extra_width = raw_width - width margin_top = int(outpainting_top/(100 + outpainting_top + outpainting_bottom) * final_height) if extra_height != 0 and (outpainting_top + outpainting_bottom) != 0: margin_top += int(outpainting_top / (outpainting_top + outpainting_bottom) * extra_height) if (margin_top + height) > final_height or outpainting_bottom == 0: margin_top = final_height - height margin_left = int(outpainting_left/(100 + outpainting_left + outpainting_right) * final_width) if extra_width != 0 and (outpainting_left + outpainting_right) != 0: margin_left += int(outpainting_left / (outpainting_left + outpainting_right) * extra_height) if (margin_left + width) > final_width or outpainting_right == 0: margin_left = final_width - width return height, width, margin_top, margin_left def calculate_new_dimensions(canvas_height, canvas_width, image_height, image_width, fit_into_canvas, block_size = 16): if fit_into_canvas == None: # return image_height, image_width return canvas_height, canvas_width if fit_into_canvas: scale1 = min(canvas_height / image_height, canvas_width / image_width) scale2 = min(canvas_width / image_height, canvas_height / image_width) scale = max(scale1, scale2) else: scale = (canvas_height * canvas_width / (image_height * image_width))**(1/2) new_height = round( image_height * scale / block_size) * block_size new_width = round( image_width * scale / block_size) * block_size return new_height, new_width def resize_and_remove_background(img_list, budget_width, budget_height, rm_background, ignore_first, fit_into_canvas = False ): if rm_background: session = new_session() output_list =[] for i, img in enumerate(img_list): width, height = img.size if fit_into_canvas: white_canvas = np.ones((budget_height, budget_width, 3), dtype=np.uint8) * 255 scale = min(budget_height / height, budget_width / width) new_height = int(height * scale) new_width = int(width * scale) resized_image= img.resize((new_width,new_height), resample=Image.Resampling.LANCZOS) top = (budget_height - new_height) // 2 left = (budget_width - new_width) // 2 white_canvas[top:top + new_height, left:left + new_width] = np.array(resized_image) resized_image = Image.fromarray(white_canvas) else: scale = (budget_height * budget_width / (height * width))**(1/2) new_height = int( round(height * scale / 16) * 16) new_width = int( round(width * scale / 16) * 16) resized_image= img.resize((new_width,new_height), resample=Image.Resampling.LANCZOS) if rm_background and not (ignore_first and i == 0) : # resized_image = remove(resized_image, session=session, alpha_matting_erode_size = 1,alpha_matting_background_threshold = 70, alpha_foreground_background_threshold = 100, alpha_matting = True, bgcolor=[255, 255, 255, 0]).convert('RGB') resized_image = remove(resized_image, session=session, alpha_matting_erode_size = 1, alpha_matting = True, bgcolor=[255, 255, 255, 0]).convert('RGB') output_list.append(resized_image) #alpha_matting_background_threshold = 30, alpha_foreground_background_threshold = 200, return output_list def str2bool(v): """ Convert a string to a boolean. Supported true values: 'yes', 'true', 't', 'y', '1' Supported false values: 'no', 'false', 'f', 'n', '0' Args: v (str): String to convert. Returns: bool: Converted boolean value. Raises: argparse.ArgumentTypeError: If the value cannot be converted to boolean. """ if isinstance(v, bool): return v v_lower = v.lower() if v_lower in ('yes', 'true', 't', 'y', '1'): return True elif v_lower in ('no', 'false', 'f', 'n', '0'): return False else: raise argparse.ArgumentTypeError('Boolean value expected (True/False)') import sys, time # Global variables to track download progress _start_time = None _last_time = None _last_downloaded = 0 _speed_history = [] _update_interval = 0.5 # Update speed every 0.5 seconds def progress_hook(block_num, block_size, total_size, filename=None): """ Simple progress bar hook for urlretrieve Args: block_num: Number of blocks downloaded so far block_size: Size of each block in bytes total_size: Total size of the file in bytes filename: Name of the file being downloaded (optional) """ global _start_time, _last_time, _last_downloaded, _speed_history, _update_interval current_time = time.time() downloaded = block_num * block_size # Initialize timing on first call if _start_time is None or block_num == 0: _start_time = current_time _last_time = current_time _last_downloaded = 0 _speed_history = [] # Calculate download speed only at specified intervals speed = 0 if current_time - _last_time >= _update_interval: if _last_time > 0: current_speed = (downloaded - _last_downloaded) / (current_time - _last_time) _speed_history.append(current_speed) # Keep only last 5 speed measurements for smoothing if len(_speed_history) > 5: _speed_history.pop(0) # Average the recent speeds for smoother display speed = sum(_speed_history) / len(_speed_history) _last_time = current_time _last_downloaded = downloaded elif _speed_history: # Use the last calculated average speed speed = sum(_speed_history) / len(_speed_history) # Format file sizes and speed def format_bytes(bytes_val): for unit in ['B', 'KB', 'MB', 'GB']: if bytes_val < 1024: return f"{bytes_val:.1f}{unit}" bytes_val /= 1024 return f"{bytes_val:.1f}TB" file_display = filename if filename else "Unknown file" if total_size <= 0: # If total size is unknown, show downloaded bytes speed_str = f" @ {format_bytes(speed)}/s" if speed > 0 else "" line = f"\r{file_display}: {format_bytes(downloaded)}{speed_str}" # Clear any trailing characters by padding with spaces sys.stdout.write(line.ljust(80)) sys.stdout.flush() return downloaded = block_num * block_size percent = min(100, (downloaded / total_size) * 100) # Create progress bar (40 characters wide to leave room for other info) bar_length = 40 filled = int(bar_length * percent / 100) bar = '█' * filled + '░' * (bar_length - filled) # Format file sizes and speed def format_bytes(bytes_val): for unit in ['B', 'KB', 'MB', 'GB']: if bytes_val < 1024: return f"{bytes_val:.1f}{unit}" bytes_val /= 1024 return f"{bytes_val:.1f}TB" speed_str = f" @ {format_bytes(speed)}/s" if speed > 0 else "" # Display progress with filename first line = f"\r{file_display}: [{bar}] {percent:.1f}% ({format_bytes(downloaded)}/{format_bytes(total_size)}){speed_str}" # Clear any trailing characters by padding with spaces sys.stdout.write(line.ljust(100)) sys.stdout.flush() # Print newline when complete if percent >= 100: print() # Wrapper function to include filename in progress hook def create_progress_hook(filename): """Creates a progress hook with the filename included""" global _start_time, _last_time, _last_downloaded, _speed_history # Reset timing variables for new download _start_time = None _last_time = None _last_downloaded = 0 _speed_history = [] def hook(block_num, block_size, total_size): return progress_hook(block_num, block_size, total_size, filename) return hook